1. Field of the Invention
The present invention relates to image analysis methods and systems for identifying objects in a background by selecting thresholds for a data space, generating a list of threshold pairs, using the threshold pairs to subdivide the data space into a plurality of sub-spaces, selecting at least one sub-space and multiply searching an image of the object and the background using each selected sub-space for a representation of a candidate object and validating the candidate object which has at least one predetermined attribute value of a valid object.
2. Description of the Related Art
Image analysis methods and systems, and more particularly, those methods and systems which recursively repeat the sequence of threshold selection and image searching for identifying objects in a background using thresholding are known in the art. For instance, published patent application WO 94/06562, teaches a method of entropically selecting a gray level threshold from a gray level histogram or a co-occurrence matrix, searching an image for candidate objects, validating the candidate objects, subdividing the histogram or the co-occurrence matrix and recursively repeating the threshold selection and searching steps of the method until a predetermined number of new valid objects is identified. In this published patent application, an image is recursively searched and individual thresholds are selected at each recursion.
Although thresholding is taught by WO 94/06562, the disclosure of this application is limited to entropic threshold selection combined with searching the image each time a new threshold is selected. Thus, in WO 94/06562, image searching is coupled to each threshold selection. Therefore, WO 94/06562 fails to find and identify certain valid objects under certain background conditions. Moreover, the selection of only individual thresholds increases the likelihood of selecting invalid objects.
By first selecting threshold pairs instead of individual thresholds before searching the image, a more comprehensive search of the data space of the image may be achieved, resulting in a higher likelihood that all possible objects will be found. Therefore, it would be desirable to separate the threshold selection step from the searching step, as well as to select threshold pairs as opposed to individual thresholds.